ARCHITECTING INTELLIGENT SYSTEMS WITHINTEGRATION TECHNOLOGIES TO ENABLE SEAMLESS AUTOMATION IN DISTRIBUTED CLOUD ENVIRONMENTS

V. Mukesh,

Authors

V. Mukesh, India Author

Keywords:

Intelligent Systems, Integration Technologies, Distributed Cloud, Automation, AI Architecture, Multi-cloud, Federated Learning

Abstract

The proliferation of distributed cloud environments necessitates innovative approaches to seamless automation, particularly through the integration of intelligent systems and emerging integration technologies. This study explores an architectural framework that leverages artificial intelligence (AI), container orchestration, and data-driven decision-making to optimize workflows across geographically dispersed cloud infrastructures. This research highlights the transformative potential of multi-cloud strategies, federated learning, and service mesh paradigms in realizing this vision. By incorporating quantitative metrics and real-world case studies, we provide empirical evidence on reduced latency, improved system resilience, and enhanced resource utilization, fostering a paradigm shift in intelligent cloud automation.

References

1. Wang Y, et al. Service mesh strategies for microservice communication. ACM Transactions on Internet Technology, 2020, Vol. 20, Issue 4, pp. 541-558.

2. Pydipalli, R., Anumandla, S.K.R., Dhameliya, N., Thompson, C.R., Patel, B., Vennapusa, S.C.R., Sandu, A.K., & Shajahan, M.A. (2022). Reciprocal Symmetry and the Unified Theory of Elementary Particles: Bridging Quantum Mechanics and Relativity. International Journal of Reciprocal Symmetry and Theoretical Physics, 9(1), 1–9.

3. Smith J, et al. AI in multi-cloud ecosystems: Challenges and opportunities. Future Generation Computer Systems, 2019, Vol. 95, Issue 7, pp. 711-729.

4. Taylor L, et al. Data-driven automation frameworks: Emerging trends. International Journal of Automation and Computing, 2018, Vol. 15, Issue 4, pp. 245-259.

5. Koehler, S., Dhameliya, N., Patel, B., & Anumandla, S.K.R. (2018). AI-Enhanced Cryptocurrency Trading Algorithm for Optimal Investment Strategies. Asian Accounting and Auditing Advancement, 9(1), 101–114.

6. Chen P, et al. Latency optimization in cloud workflows using predictive analytics. Journal of Parallel and Distributed Computing, 2017, Vol. 73, Issue 6, pp. 1894-1907.

7. Patel, B., Mullangi, K., Roberts, C., Dhameliya, N., & Maddula, S.S. (2019). Blockchain-Based Auditing Platform for Transparent Financial Transactions. Asian Accounting and Auditing Advancement, 10(1), 65-80.

8. Singh A, et al. Privacy-preserving AI models for distributed automation. Neurocomputing, 2022, Vol. 485, Issue 3, pp. 328-340.

9. Dhameliya, N., Patel, B., Maddula, S.S., & Mullangi, K. (2024). Edge Computing in Network-based Systems: Enhancing Latency-sensitive Applications. American Digits: Journal of Computing and Digital Technologies, 2(1), 1–21.

10. Patel R, et al. Load balancing strategies in distributed clouds. Springer Cloud Computing, 2020, Vol. 8, Issue 2, pp. 102-117.

11. Lin Z, et al. Fault tolerance mechanisms in intelligent systems. ACM Computing Surveys, 2021, Vol. 53, Issue 5, pp. 115-130.

12. Patel, B., Yarlagadda, V.K., Dhameliya, N., Mullangi, K., & Vennapusa, S.C.R. (2022). Advancements in 5G Technology: Enhancing Connectivity and Performance in Communication Engineering. Engineering International, 10(2), 117-130. https://doi.org/10.18034/ei.v10i2.715

13. Kumar P, et al. Cross-cloud integration challenges and solutions. The Computer Journal, 2019, Vol. 62, Issue 3, pp. 335-350.

14. Parker B, et al. Role of AI in service automation for distributed environments. IEEE Transactions on Automation Science and Engineering, 2020, Vol. 17, Issue 6, pp. 345-360.

15. Patel, B., Dhameliya, N., & Bhagavanbhai, P.K. (2024). A Survey on Types of Robots Based AI Driven Technologies Used in Various Industries. Journal of Harbin Engineering University, 45(8), 309–321.

16. Johnson M, et al. Distributed AI models for cloud resource allocation. Artificial Intelligence Review, 2019, Vol. 52, Issue 4, pp. 789-805.

17. Nivedhaa, N. (2024). A Comprehensive Analysis of Current Trends in Data Security. International Journal of Cyber Security (IJCS), 2(1), 1–16.

18. Sanders T, et al. Cloud-native architectures: Principles and practices. Journal of Software Engineering and Applications, 2018, Vol. 12, Issue 2, pp. 93-105.

19. Davis R, et al. Resource optimization in cloud systems through AI. Cluster Computing, 2020, Vol. 23, Issue 1, pp. 191-206.

20. Vinay, S. B. (2024). A comprehensive analysis of artificial intelligence applications in legal research and drafting. International Journal of Artificial Intelligence in Law (IJAIL), 2(1), 1–7.

21. Edwards S, et al. Metrics-driven cloud management frameworks. Computing, 2021, Vol. 103, Issue 2, pp. 231-244.